Improving the Value at Risk Forecasts: Theory and Evidence from the Financial Crisis

Abstract

The recent financial crisis has raised numerous questions about the accuracy of value-at-risk (VaR) as a tool to quantify extreme losses. In this paper we develop data-driven VaR approaches that are based on the principle of optimal combination and that provide robust and precise VaR forecasts for periods when they are needed most, such as the recent financial crisis. Within a comprehensive comparative study we provide the latest piece of empirical evidence on the performance of a wide range of standard VaR approaches and highlight the overall outperformance of the newly developed methods.

Keywords: Value-at-Risk, Optimal Forecast Combination, Quantile Regression, Method of Moments, Financial Crisis

JEL Classification: C21, C5, G01, G17, G28, G32